# An adaptive volatility method for probabilistic forecasting and its application to the M6 financial forecasting competition

**Authors:** Joseph de Vilmarest, Nicklas Werge  
**arXiv:** [2303.01855](https://arxiv.org/abs/2303.01855)  
**Date:** 2023-03-03  
**Summary page:** https://www.summarizepaper.com/fr/paper/2303.01855v1/

## Abstract

In this note, we address the problem of probabilistic forecasting using an adaptive volatility method based on classical time-varying volatility models and stochastic optimization algorithms. These principles were successfully applied in the recent M6 financial forecasting competition for both probabilistic forecasting and investment decision-making under the team named AdaGaussMC. The key points of our strategy are: (a) apply a univariate time-varying volatility model, called AdaVol, (b) obtain probabilistic forecasts of future returns, and (c) optimize the competition metrics using stochastic gradient-based algorithms. We claim that the frugality of the methods implies its robustness and consistency.